Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification

Lawgaly, Ashref, Khelifi, Fouad, Bouridane, Ahmed and Al-Maaddeed, Somaya (2021) Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification. In: 2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, Piscataway, pp. 19-24. ISBN 9781665449120, 9781665449113, 9781665449106

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Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for image authentication and source camera identification. The abundant information that the PRNU carries in terms of the frequency content makes it unique, and therefore suitable for identifying the source camera and detecting forgeries in digital images. However, PRNU estimation from smartphone videos is a challenging process due to the presence of frame-dependent information (very dark/very textured), as well as other non-unique noise components and distortions due to lossy compression. In this paper, we propose an approach that considers only the non-textured frames in estimating the PRNU because its estimation in highly textured images has been proven to be inaccurate in image forensics. Furthermore, lossy compression distortions tend to affect mainly the textured and high activity regions and consequently weakens the presence of the PRNU in such areas. The proposed technique uses a number of texture measures obtained from the Grey Level Cooccurrence Matrix (GLCM) prior to an unsupervised learning process that splits the feature space through training video frames into two different sub-spaces, i.e., the textured space and the non-textured space. Non-textured video frames are filtered out and used for estimating the PRNU. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art approach.

Item Type: Book Section
Additional Information: Funding information: This work was supported by NPRP grant # NPRP12S-0312-190332 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Uncontrolled Keywords: digital image forensics, Grey Level Co-occurrence Matrix (GLCM), Photo response non-uniformity noise, source smartphone identification, texture analysis
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: Elena Carlaw
Date Deposited: 19 Oct 2021 08:19
Last Modified: 19 Oct 2021 08:30

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